Gender pay gap analysis

Data is from https://gender-pay-gap.service.gov.uk/viewing/download

Load in packages and data

#install.packages("tidyverse")
#install.packages("janitor")

library(tidyverse)

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## ✔ readr   2.1.3     ✔ forcats 0.5.2
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library(janitor)

## 
## Attaching package: 'janitor'
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## The following objects are masked from 'package:stats':
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##     chisq.test, fisher.test

# here is how I would read the data files one at a time
gender_pay_2017_18 <- read_csv("data/UK Gender Pay Gap Data - 2017 to 2018.csv")

## Rows: 10219 Columns: 27
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): EmployerName, Address, PostCode, CompanyNumber, SicCodes, CompanyL...
## dbl (15): EmployerId, DiffMeanHourlyPercent, DiffMedianHourlyPercent, DiffMe...
## lgl  (1): SubmittedAfterTheDeadline
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

gender_pay_2018_19 <- read_csv("data/UK Gender Pay Gap Data - 2018 to 2019.csv")

## Rows: 10459 Columns: 27
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): EmployerName, Address, PostCode, CompanyNumber, SicCodes, CompanyL...
## dbl (15): EmployerId, DiffMeanHourlyPercent, DiffMedianHourlyPercent, DiffMe...
## lgl  (1): SubmittedAfterTheDeadline
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# read in multiple files in one:
list_of_files <- list.files(path = "data", # specify the folder path
                            pattern = "\\.csv$", # only return files that end in csv
                            full.names = TRUE # foldeer path attached to the beginning of the file name
                            )

pay_combined <- read_csv(list_of_files, id = "file_name")

## Rows: 59315 Columns: 28
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): EmployerName, Address, PostCode, CompanyNumber, SicCodes, CompanyL...
## dbl (15): EmployerId, DiffMeanHourlyPercent, DiffMedianHourlyPercent, DiffMe...
## lgl  (1): SubmittedAfterTheDeadline
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

pay_combined

Tidy the data

pay_trimmed <- pay_combined %>% 
  clean_names() %>% 
  mutate(year_starting = as.numeric(str_extract(file_name, "[0-9]+")), .before = 1) %>%  # take the first year listed in the file name
  select(contains(c("year", "post", "percent", "quartile", "size"))) # selecting desired columns

pay_trimmed

Find out the Scottish postcodes

scottish_postcodes <- c("AB", "DD", "PH", "FK", "G", "PA", "DG", "KA", "ML", "DG", "EH", "KY", "IV", "TD", "ZE", "HS")

Cut down the postcode column to just the first section and use this to determine if a company is in scotland or not:

pay_region <- pay_trimmed %>% 
  mutate(post_code = str_extract(post_code, "[A-Z0-9]+")) %>% 
  mutate(post_code = str_remove_all(post_code, "[0-9]+")) %>% 
  mutate(region = if_else(post_code %in% scottish_postcodes, "Scotland", "Rest of UK"), .after = post_code) %>% 
  select(-post_code)

pay_region

What pay differences can we see in Scotland year on year?

diff_mean_hourly_percent: mean % difference between male and female hourly pay (negative = women’s mean hourly pay is higher)

pay_region %>% 
  filter(region == "Scotland") %>% 
  ggplot() +
  geom_boxplot(aes(x = year_starting, y = diff_mean_hourly_percent, group = year_starting), colour = "blue", alpha = 0.5) +
  labs(
    title = "Gender pay differences across Scottish companies",
    subtitle = "2017 - 2023\n",
    x = "\nFinancial year starting",
    y = "Mean % difference in hourly pay\n(M - F)"
  ) +
  scale_x_continuous(breaks = c(2017, 2018, 2019, 2020, 2021, 2022, 2023)) +
  theme_minimal() +
  theme(panel.grid = element_blank(),
        axis.line = element_line(colour = "black")) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  coord_flip()

Let’s check out 2023

pay_region %>% 
  filter(region == "Scotland") %>% 
  group_by(year_starting) %>% 
  summarise(nrows = n())

Are there any differences in bonuses given in Scottish companies?

male_bonus_percent: % of male employees paid a bonus

female_bonus_percent: % of female employees paid a bonus

pay_region %>% 
  filter(region == "Scotland") %>% 
  select(male_bonus_percent, female_bonus_percent) %>% 
  filter(male_bonus_percent != 0 & female_bonus_percent != 0) %>% 
  pivot_longer(cols = c("male_bonus_percent",
                        "female_bonus_percent"),
               names_to = "gender",
               values_to = "bonus_percent") %>% 
  ggplot() +
  geom_boxplot(aes(x = gender, y = bonus_percent, colour = gender), linewidth = 1.5, show.legend = FALSE) +
  labs(
    title = "Bonuses paid across Scottish companies",
    subtitle = "2017 - 2023\n",
    x = "",
    y = "% of employees paid a bonus"
  ) +
  scale_x_discrete(labels = c("Women", "Men")) +
  theme_minimal() +
  theme(panel.grid = element_blank(),
        axis.line = element_line(colour = "black"))

Can see that there is very little difference in the bonuses paid to men and women across Scottish companies.

Does company size affect pay disparity?

pay_region %>% 
  filter(region == "Scotland") %>% 
  mutate(employer_size = factor(employer_size, levels = c("Less than 250", "250 to 499", "500 to 999", "1000 to 4999", "5000 to 19,999", "20,000 or more", "Not Provided"))) %>% 
  filter(!employer_size %in% c("Not Provided", NA)) %>% 
  ggplot() +
  geom_boxplot(aes(x = employer_size, y = diff_mean_hourly_percent), colour = "blue") +
  labs(
    title = "Gender pay differences across Scottish companies",
    subtitle = "2017 - 2023\n",
    x = "\nCompany size",
    y = "Mean % difference in hourly pay\n(M - F)"
  ) +
  theme_minimal() +
  theme(panel.grid = element_blank(),
        axis.line = element_line(colour = "black")) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  coord_flip()

This plot appears to show that the larger a company is, the larger the pay gap between men and women (with men earning more per hour).

How does Scotland compare to the rest of the UK year on year?

A line plot could be good for this

How does Edinburgh compare to London when it comes to the gender pay gap?